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Pb-chalcogenide colloidal quantum dots (CQDs) are attractive materials to be used as tuneable laser media across the infrared spectrum. However, excessive nonradiative Auger recombination due to the presence of trap states outcompetes light amplification by rapidly annihilating the exciton population, leading to high gain thresholds. Here, we employ a binary blend of CQDs and ZnO nanocrystals in order to passivate the in-gap trap states of PbS-CQD gain medium. Using transient absorption, we measure a five-fold increase in Auger lifetime demonstrating the suppression of trap-assisted Auger recombination. By doing so, we achieve a two-fold reduction in amplified spontaneous emission (ASE) threshold. Finally, by integrating our proposed binary blend to a DFB resonator, we demonstrate single-mode lasing emission at 1650 nm with a linewidth of 1.23 nm (0.62 meV), operating at a low lasing threshold of ~385 {\mu}J.cm-2. The Auger suppression in this system has allowed to achieve unprecedented lasing emission stability for a CQD laser with recorded continuous operation of 5 hours at room temperature and ambient conditions.

Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model. Exact lifted inference methods, like their propositional counterparts, work by recursively decomposing the model and the problem. In the propositional case, there exist formal structures, such as decomposition trees (dtrees), that represent such a decomposition and allow us to determine the complexity of inference a priori. However, there is currently no equivalent structure nor analogous complexity results for lifted inference. In this paper, we introduce FO-dtrees, which upgrade propositional dtrees to the first-order level. We show how these trees can characterize a lifted inference solution for a probabilistic logical model (in terms of a sequence of lifted operations), and make a theoretical analysis of the complexity of lifted inference in terms of the novel notion of lifted width for the tree. ispartof: Advances in Neural Information Processing Systems vol:abs/1306.0751 issue:26 pages:1-9 ispartof: location:Lake Tahoe, Nevada, USA status: published

Lifting aims at improving the efficiency of probabilistic inference by exploiting symmetries in the model. Various methods for lifted probabilistic inference have been proposed, but our understanding of these methods and the relationships between them is still limited, compared to their propositional counterparts. The only existing theoretical characterization of lifting is a completeness result for weighted first-order model counting. This paper addresses the question whether the same completeness result holds for other lifted inference algorithms. We answer this question positively for lifted variable elimination (LVE). Our proof relies on introducing a novel inference operator for LVE. ispartof: pages:572- ispartof: Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, JMLR workshop and conference proceedings vol:31 pages:572- ispartof: International Conference on Artificial Intelligence and Statistics (AISTATS) location:Scottsdale, USA date:29 Apr - 1 May 2013 status: published

A probabilistic program often gives rise to a complicated underlying probabilistic model. Performing inference in such a model is challenging. One solution to this problem is lifted inference which improves tractability by exploiting symmetries in the underlying model. Our group is pursuing a lifted approach to inference for probabilistic logic programs. ispartof: Proceedings of the NIPS Probabilistic Programming Workshop ispartof: Probabilistic Programming Workshop location:Lake Tahoe date:7 Dec - 8 Dec 2012 status: published

Various methods for lifted probabilistic inference have been proposed, but our understanding of these methods and the relationships between them is still limited, compared to their propositional counterparts. The only existing theoretical characterization of lifting is for weighted first-order model counting (WFOMC), which was shown to be complete domain-lifted for the class of 2-logvar models. This paper makes two contributions to lifted variable elimination (LVE). First, we introduce a novel inference operator called group inversion. Second, we prove that LVE augmented with this operator is complete in the same sense as WFOMC.

Lifted probabilistic inference methods exploit symmetries in the structure of probabilistic models to perform inference more efficiently. In lifted variable elimination the symmetry among a group of interchangeable random variables is captured by counting formulas, and exploited by operations that handle such formulas. In this paper we generalize the structure of counting formulas and present a set of inference operators that introduce and eliminate these formulas from the model. This generalization expands the range of problems that can be solved in a lifted way. Our work is closely related to the recently introduced method of joint conversion. Due to its more fine grained formulation, however, our approach can provide more efficient solutions than joint conversion. ispartof: pages:1-8 ispartof: Proceedings of the Second International Workshop on Statistical Relational AI (StaRAI) pages:1-8 ispartof: International Workshop on Statistical Relational AI (StaRAI-12) location:Catalina Island, CA, USA date:18 Aug - 18 Aug 2012 status: published

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once for each group, as opposed to once for each variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language. Existing approaches for exact lifted inference rely on (in)equality constraints. We show how inference methods can be generalized to work with arbitrary constraints. This allows them to capture a broader range of symmetries, leading to more opportunities for lifting. We empirically demonstrate that this improves inference efficiency with orders of magnitude, allowing exact inference in cases where until now only approximate inference was feasible. ispartof: pages:1194-1202 ispartof: Proceedings of the fifteenth international conference on Artificial Intelligence and Statistics, JMLR workshop and conference proceedings vol:22 pages:1194-1202 ispartof: International conference on artificial intelligence and statistics (AISTATS) location:La Palma, Canary Islands date:21 Apr - 23 Apr 2012 status: published

Probabilistic logical languages provide powerful formalisms for knowledge representation and learning. Yet performing inference in these languages is extremely costly, especially if it is done at the propositional level. Lifted inference algorithms, which avoid repeated computation by treating indistinguishable groups of objects as one, help mitigate this cost. Seeking inspiration from logical inference, where lifted inference (e.g., resolution) is commonly performed, we develop a model theoretic approach to probabilistic lifted inference. Our algorithm compiles a first-order probabilistic theory into a first-order deterministic decomposable negation normal form (d-DNNF) circuit. Compilation offers the advantage that inference is polynomial in the size of the circuit. Furthermore, by borrowing techniques from the knowledge compilation literature our algorithm effectively exploits the logical structure (e.g., context-specific independencies) within the first-order model, which allows more computation to be done at the lifted level. An empirical comparison demonstrates the utility of the proposed approach. acceptance rate 17% ispartof: pages:2178-2185 ispartof: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence pages:2178-2185 ispartof: International Joint Conference on Artificial Intelligence (IJCAI) location:Barcelona, Spain date:16 Jul - 22 Jul 2011 status: published

Many approaches to probabilistic logical learning have been proposed by now, and several of these have been implemented into powerful learning and inference systems. Given this state of the art, it appears natural to start using these systems for solving concrete problems. This paper presents some results of a case study where several probabilistic logical learning systems have been applied to a seemingly simple problem that exhibits both probabilistic and relational aspects. The results are surprisingly negative: none of the systems we have tried could adequately handle the problem at hand. We discuss the reasons for this. This leads to several conclusions. First, still more effort must be invested in developing full-fledged implementations that can handle a wide range of realistic problems. Second, the intrinsic limitations of certain approaches may not yet be fully understood. Third, the problem we discuss here may be an interesting application for probabilistic logical learning systems, and we invite other researchers to use it as a benchmark for evaluating the applicability of their favorite systems. ispartof: CW Reports nrpages: 8 status: published

Efficient probabilistic inference is key to the success of statistical relational learning. One issue that affects inference cost is the presence of irrelevant random variables. The Bayes-ball algorithm can identify such irrelevant variables in a propositional Bayesian network. This paper presents a lifted version of Bayes-ball, which works directly on the first-order level, and shows how this algorithm applies to CP-logic inference. ispartof: pages:1-3 ispartof: International Workshop on Statistical Relational Learning 2009 pages:1-3 ispartof: International Workshop on Statistical Relational Learning (SRL-2009) location:Leuven, Belgium date:2 Jul - 4 Jul 2009 status: published

A phototransistor built by the assembly of 2D colloidal semiconductor quantum wells (CQWs) on a single layer of 2D transition metal dichalcogenide (TMD) is displayed. This hybrid device architecture exhibits high efficiency in Forster resonance energy transfer (FRET) enabling superior performance in terms of photoresponsivity and detectivity. Here, a thin film of CdSe/CdS CQWs acts as a sensitizer layer on top of the MoS2 monolayer based field-effect transistor, where this CQWs-MoS2 structure allows for strong light absorption in CQWs in the operating spectral region and strong dipole-to-dipole coupling between MoS2 and CQWs resulting in enhanced photoresponsivity of one order of magnitude (11-fold) at maximum gate voltage (V-BG = 2 V) and two orders of magnitude (approximate to 5 x 10(2)) at V-BG = -1.5 V, and tenfold enhanced specific detectivity. The illumination power-dependent characterization of this hybrid device reveals that the thin layer of CQWs dominates the photogating mechanism compared to the photoconductivity effect on detection performance. Such hybrid designs hold great promise for 2D-material based photodetectors to reach high performance and find use in optoelectronic applications.

We utilized time-resolved photoluminescence (TRPL) spectroscopy to study the excitonic circular polarization (PX) from CdSe/CdMnS core/shell nanoplatelets (NPLs) with a bilayer core. This allows an extensive study of the emission dynamics as a function of magnetic field, temperature, doping concentration, and excitation wavelength. In the presence of an external magnetic field, pulsed excitation below the shell gap results in near-zero excitonic circular polarization PX at all time delays. In contrast, pulsed excitation with photon energy larger than the shell gap results in a rapid (100 ps) buildup of the excitonic circular polarization which subsequently remains constant at a level of up to 40%. We propose a model to describe the dynamics which takes into account the exchange interaction between carrier and magnetic ion (Mn) spins. The studied system exhibits a fast switchable excitonic circular polarization, implying possible applications in lasers and light emitting diodes.

Solution-processed type-II quantum wells exhibit outstanding optical properties, which make them promising candidates for light-generating applications including lasers and LEDs. However, they may suffer from poor colloidal stability under ambient conditions and show strong tendency to assemble into face-to-face stacks. In this work, to resolve the colloidal stability and uncontrolled stacking issues, we proposed and synthesized CdSe/CdSe1–xTex/CdS core/multicrown heteronanoplatelets (NPLs), controlling the amount of Te up to 50% in the crown without changing their thicknesses, which significantly increases their colloidal and photostability under ambient conditions and at the same time preserving their attractive optical properties. Confirming the final lateral growth of CdS sidewalls with X-ray photoelectron spectroscopy, energy-dispersive analysis, and photoelectron excitation spectroscopy, we found that the successful coating of this CdS crown around the periphery of conventional type-II NPLs prevents the unwanted formation of needle-like stacks, which results in reduction of the undesired scattering losses in thin-film samples of these NPLs. Owing to highly efficient exciton funneling from the outmost CdS crown accompanied by the reduced scattering and very low waveguide loss coefficient (∼18 cm–1), ultralow optical gain thresholds of multicrown type-II NPLs were achieved to be as low as 4.15 μJ/cm2 and 2.48 mJ/cm2 under one- and two-photon absorption pumping, respectively. These findings indicate that the strategy of using engineered advanced heterostructures of nanoplatelets provides solutions for improved colloidal stability and enables enhanced photonic performance. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) Accepted version The authors gratefully acknowledge the financial support in part from Singapore National Research Foundation under the programs of NRF-NRFI2016-08 and the Science and Engineering Research Council, Agency for Science, Technology and Research (A*STAR) of Singapore and in part from TUBITAK 114F326 and 115E679. H.V.D. also acknowledges support from TUBA. K.G. and Y.K. acknowledge support from TUBITAK BIDEB.

Recently, engineering the surface of nanotherapeutics with biologics to provide them with superior biocompatibility and targeting towards pathological tissues has gained significant popularity. Although the functionalization of drug delivery vectors with cellular materials has been shown to provide synthetic particles with unique biological properties, these approaches may have undesirable immunological repercussions upon systemic administration. Herein, we comparatively analyzed unmodified multistage nanovectors and particles functionalized with murine and human leukocyte cellular membrane, dubbed Leukolike Vectors (LLV), and the immunological effects that may arise in vitro and in vivo. Previously, LLV demonstrated an avoidance of opsonization and phagocytosis, in addition to superior targeting of inflammation and prolonged circulation. In this work, we performed a comprehensive evaluation of the importance of the source of cellular membrane in increasing their systemic tolerance and minimizing an inflammatory response. Time-lapse microscopy revealed LLV developed using a cellular coating derived from a murine (i.e., syngeneic) source resulted in an active avoidance of uptake by macrophage cells. Additionally, LLV composed of a murine membrane were found to have decreased uptake in the liver with no significant effect on hepatic function. As biomimicry continues to develop, this work demonstrates the necessity to consider the source of biological material in the development of future drug delivery carriers. © 2015.